请温文尔雅,初学滑冰。计算客户流失,使用不同的roc_auc评分,我得到了3个不同的分数。分数1和3接近,与分数2有显著差异。感谢您的指导,为什么会有这样的差异,哪一个可能是首选?非常感谢!
from sklearn.model_selection import cross_val_score
from sklearn.metrics import roc_auc_score
param_grid = {'n_estimators': range(10, 510, 100)}
grid_search = GridSearchCV(estimator=RandomForestClassifier(criterion='gini', max_features='auto',
random_state=20), param_grid=param_grid, scoring='roc_auc', n_jobs=4, iid=False, cv=5, verbose=0)
grid_search.fit(self.dataset_train, self.churn_train)
score_roc_auc = np.mean(cross_val_score(grid_search, self.dataset_test, self.churn_test, cv=5, scoring='roc_auc'))
"^^^ SCORE1 - 0.6395751751133528
pred = grid_search.predict(self.dataset_test)
score_roc_auc_2 = roc_auc_score(self.churn_test, pred)
"^^^ SCORE2 - 0.5063261397640454
print("grid best score ", grid_search.best_score_)
"^^^ SCORE3 - 0.6473102070034342
发布于 2018-03-29 10:14:20
我相信下面的链接可以回答这个问题,在较小的拆分上,GridSearchCV中的折叠和得分是什么?
Difference in ROC-AUC scores in sklearn RandomForestClassifier vs. auc methods
https://stackoverflow.com/questions/49544509
复制相似问题